kernels are now consistent with pep8 and common reason

GPy/kern/parts/symmetric.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2012 James Hensman
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from kernpart import Kernpart
+import numpy as np
+
+class Symmetric(Kernpart):
+    """
+    Symmetrical kernels
+
+    :param k: the kernel to symmetrify
+    :type k: Kernpart
+    :param transform: the transform to use in symmetrification (allows symmetry on specified axes)
+    :type transform: A numpy array (input_dim x input_dim) specifiying the transform
+    :rtype: Kernpart
+
+    """
+    def __init__(self,k,transform=None):
+        if transform is None:
+            transform = np.eye(k.input_dim)*-1.
+        assert transform.shape == (k.input_dim, k.input_dim)
+        self.transform = transform
+        self.input_dim = k.input_dim
+        self.num_params = k.num_params
+        self.name = k.name + '_symm'
+        self.k = k
+        self._set_params(k._get_params())
+
+    def _get_params(self):
+        """return the value of the parameters."""
+        return self.k._get_params()
+
+    def _set_params(self,x):
+        """set the value of the parameters."""
+        self.k._set_params(x)
+
+    def _get_param_names(self):
+        """return parameter names."""
+        return self.k._get_param_names()
+
+    def K(self,X,X2,target):
+        """Compute the covariance matrix between X and X2."""
+        AX = np.dot(X,self.transform)
+        if X2 is None:
+            X2 = X
+            AX2 = AX
+        else:
+            AX2 = np.dot(X2, self.transform)
+        self.k.K(X,X2,target)
+        self.k.K(AX,X2,target)
+        self.k.K(X,AX2,target)
+        self.k.K(AX,AX2,target)
+
+    def dK_dtheta(self,dL_dK,X,X2,target):
+        """derivative of the covariance matrix with respect to the parameters."""
+        AX = np.dot(X,self.transform)
+        if X2 is None:
+            X2 = X
+            ZX2 = AX
+        else:
+            AX2 = np.dot(X2, self.transform)
+        self.k.dK_dtheta(dL_dK,X,X2,target)
+        self.k.dK_dtheta(dL_dK,AX,X2,target)
+        self.k.dK_dtheta(dL_dK,X,AX2,target)
+        self.k.dK_dtheta(dL_dK,AX,AX2,target)
+
+
+    def dK_dX(self,dL_dK,X,X2,target):
+        """derivative of the covariance matrix with respect to X."""
+        AX = np.dot(X,self.transform)
+        if X2 is None:
+            X2 = X
+            ZX2 = AX
+        else:
+            AX2 = np.dot(X2, self.transform)
+        self.k.dK_dX(dL_dK, X, X2, target)
+        self.k.dK_dX(dL_dK, AX, X2, target)
+        self.k.dK_dX(dL_dK, X, AX2, target)
+        self.k.dK_dX(dL_dK, AX ,AX2, target)
+
+    def Kdiag(self,X,target):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        foo = np.zeros((X.shape[0],X.shape[0]))
+        self.K(X,X,foo)
+        target += np.diag(foo)
+
+    def dKdiag_dX(self,dL_dKdiag,X,target):
+        raise NotImplementedError
+
+    def dKdiag_dtheta(self,dL_dKdiag,X,target):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        raise NotImplementedError